There are many systems that identify popular content in the Web or recommend popular content. Such systems are often based on determining or evaluating factors that include when, where, and how many users are viewing or interacting with particular content over some period of time. Similar issues are addressed by various click prediction systems that evaluate web usage information in an attempt to compute the probability that a given document, or an advertisement, in a search-result page is clicked on after a user enters some query. Further, the click models used for click prediction are sometimes personalized to particular users to enable the use of a user-specific click through rate (CTR).
Information retrieval has been address using various techniques. For example, Latent Semantic Analysis (LSA) provides a semantic model designed for various information retrieval (IR) based tasks. Examples of generative topic models used for IR include probabilistic LSA, Latent Dirichlet Allocation (LDA), etc. In addition, some of these models have been extended to handle cross-lingual cases to retrieve information from pairs of corresponding documents in different languages.
Various deep learning techniques have been used to evaluate training data to discover hidden structures and associated features at different levels of abstraction for a variety of tasks. For example, some of these techniques use deep neural networks or other deep learning techniques to discover hierarchical semantic structures embedded in queries and documents.